Skip to main content
SpringerLink
Log in

Systems Immunology

  • Chapter
  • First Online:
Theoretical and Applied Aspects of Systems Biology

Part of the book series: Computational Biology ((COBO,volume 27))

Abstract

The advance of systems biology approaches now means that much of the immune response to pathogens and vaccines can be assessed. Modern immunologists have at their disposal an arsenal of high-throughput technologies and tools that generate data relating the quantities of genes, metabolites and proteins within immune cells. The challenge posed is how to interpret this abundance of data to accurately understand and predict the immune response. Systems immunology is the discipline that uses computational and mathematical approaches to integrate these measurements and explain the nonintuitive interactions between biological components. In this chapter we will provide an overview of this interdisciplinary approach, its challenges, and highlight some of the applications of systems biology to assess the complexity of our immune system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Iwasaki A, Medzhitov R. Control of adaptive immunity by the innate immune system. Nat Immunol. 2015;16:343–53.

    Article  Google Scholar 

  2. Chaplin DD. Overview of the immune response. J Allergy Clin Immunol. 2010;125:S3–23.

    Article  Google Scholar 

  3. Smith-Garvin JE, Koretzky GA, Jordan MS. T cell activation. Annu Rev Immunol. 2009;27:591–619.

    Article  Google Scholar 

  4. Metz PJ, Arsenio J, Kakaradov B, Kim SH, Remedios KA, Oakley K, et al. Regulation of asymmetric division and CD8+ T lymphocyte fate specification by protein kinase Cζ and protein kinase Cλ/ι. J Immunol. 2015;194:2249–59.

    Article  Google Scholar 

  5. von Andrian UH, Mempel TR. Homing and cellular traffic in lymph nodes. Nat Rev Immunol. 2003;3:867–78.

    Article  Google Scholar 

  6. Krummel MF, Bartumeus F, Gérard A. T cell migration, search strategies and mechanisms. Nat Rev Immunol. 2016;16:193–201.

    Article  Google Scholar 

  7. Gonzalez SF, Degn SE, Pitcher LA, Woodruff M, Heesters BA, Carroll MC. Trafficking of B cell antigen in lymph nodes. Annu Rev Immunol. 2011;29:215–33.

    Article  Google Scholar 

  8. Hoffman W, Lakkis FG, Chalasani G. B cells, antibodies, and more. Clin J Am Soc Nephrol. 2016;11:137–54.

    Article  Google Scholar 

  9. Allman D, Pillai S. Peripheral B cell subsets. Curr Opin Immunol. 2008;20:149–57.

    Article  Google Scholar 

  10. Golubovskaya V, Wu L. Different subsets of T cells, memory, effector functions, and CAR-T immunotherapy. Cancers (Basel). 2016;8:E36.

    Article  Google Scholar 

  11. Vidal M, Cusick ME, Barabási A-L. Interactome networks and human disease. Cell. 2011;144:986–98.

    Article  Google Scholar 

  12. Goldberg AD, Allis CD, Bernstein E. Epigenetics: a landscape takes shape. Cell. 2007;128:635–8.

    Article  Google Scholar 

  13. Cech TR, Steitz JA. The noncoding RNA revolution-trashing old rules to forge new ones. Cell. 2014;157:77–94.

    Article  Google Scholar 

  14. de Sousa AR, Penalva LO, Marcotte EM, Vogel C. Global signatures of protein and mRNA expression levels. Mol BioSyst. 2009;5:1512–26.

    Google Scholar 

  15. Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol Syst Biol. 2013;9:640.

    Article  Google Scholar 

  16. Elhanati Y, Murugan A, Callan CG, Mora T, Walczak AM. Quantifying selection in immune receptor repertoires. Proc Natl Acad Sci U S A. 2014;111:9875–80.

    Article  Google Scholar 

  17. Roy B, Neumann RS, Snir O, Iversen R, Sandve GK, Lundin KEA, et al. High-throughput single-cell analysis of B cell receptor usage among autoantigen-specific plasma cells in celiac disease. J Immunol. 2017;199:782–91.

    Article  Google Scholar 

  18. Raha D, Hong M, Snyder M. ChIP-Seq: a method for global identification of regulatory elements in the genome. Curr Protoc Mol Biol. 2010;Chapter 21:Unit 21.19.1-14. https://doi.org/10.1002/0471142727.mb2119s91

  19. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.

    Article  Google Scholar 

  20. Martinez FO, Gordon S, Locati M, Mantovani A. Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression. J Immunol. 2006;177:7303–11.

    Article  Google Scholar 

  21. Zhang JA, Mortazavi A, Williams BA, Wold BJ, Rothenberg EV. Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity. Cell. 2012;149:467–82.

    Article  Google Scholar 

  22. Watchmaker PB, Lahl K, Lee M, Baumjohann D, Morton J, Kim SJ, et al. Comparative transcriptional and functional profiling defines conserved programs of intestinal DC differentiation in humans and mice. Nat Immunol. 2014;15:98–108.

    Article  Google Scholar 

  23. Isoda T, Moore AJ, He Z, Chandra V, Aida M, Denholtz M, et al. Non-coding transcription instructs chromatin folding and compartmentalization to dictate enhancer-promoter communication and T cell fate. Cell. 2017;171:103–19. e18

    Article  Google Scholar 

  24. Wu H, Neilson JR, Kumar P, Manocha M, Shankar P, Sharp PA, et al. miRNA profiling of naïve, effector and memory CD8 T cells. PLoS One. 2007;2:e1020.

    Article  Google Scholar 

  25. Gutiérrez-Vázquez C, Rodríguez-Galán A, Fernández-Alfara M, Mittelbrunn M, Sánchez-Cabo F, Martínez-Herrera DJ, et al. miRNA profiling during antigen-dependent T cell activation: a role for miR-132-3p. Sci Rep. 2017;7:3508.

    Article  Google Scholar 

  26. Lu Y, Liu X, Xie M, Liu M, Ye M, Li M, et al. The NF-κB-responsive long noncoding RNA FIRRE regulates posttranscriptional regulation of inflammatory gene expression through interacting with hnRNPU. J Immunol. 2017;199:3571–82.

    Article  Google Scholar 

  27. Rieckmann JC, Geiger R, Hornburg D, Wolf T, Kveler K, Jarrossay D, et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat Immunol. 2017;18:583–93.

    Article  Google Scholar 

  28. Veenstra TD. Metabolomics: the final frontier? Genome Med. 2012;4:40.

    Article  Google Scholar 

  29. Jung J, Kim SH, Lee HS, Choi GS, Jung YS, Ryu DH, et al. Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis. Clin Exp Allergy. 2013;43:425–33.

    Article  Google Scholar 

  30. Stephens NS, Siffledeen J, Su X, Murdoch TB, Fedorak RN, Slupsky CM. Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J Crohns Colitis. 2013;7:e42–8.

    Article  Google Scholar 

  31. Menni C, Fauman E, Erte I, Perry JRB, Kastenmüller G, Shin S-Y, et al. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes. 2013;62:4270–6.

    Article  Google Scholar 

  32. Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016;165:780–91.

    Article  Google Scholar 

  33. Bruggeman FJ, Westerhoff HV. The nature of systems biology. Trends Microbiol. 2007;15:45–50.

    Article  Google Scholar 

  34. Davis MM, Tato CM, Furman D. Systems immunology: just getting started. Nat Immunol. 2017;18:725–32.

    Article  Google Scholar 

  35. Germain RN. Will systems biology deliver its promise and contribute to the development of new or improved vaccines? What really constitutes the study of “systems biology” and how might such an approach facilitate vaccine design. Cold Spring Harb Perspect Biol. 2017. https://doi.org/10.1101/cshperspect.a033308

  36. Lever M, Lim H-S, Kruger P, Nguyen J, Trendel N, Abu-Shah E, et al. Architecture of a minimal signaling pathway explains the T-cell response to a 1 million-fold variation in antigen affinity and dose. Proc Natl Acad Sci U S A. 2016;113:E6630–8.

    Article  Google Scholar 

  37. François P, Voisinne G, Siggia ED, Altan-Bonnet G, Vergassola M. Phenotypic model for early T-cell activation displaying sensitivity, specificity, and antagonism. Proc Natl Acad Sci U S A. 2013;110:E888–97.

    Article  Google Scholar 

  38. Mukhopadhyay H, de Wet B, Clemens L, Maini PK, Allard J, van der Merwe PA, et al. Multisite phosphorylation modulates the T cell receptor ζ-chain potency but not the Switchlike response. Biophys J. 2016;110:1896–906.

    Article  Google Scholar 

  39. Chevrier N, Mertins P, Artyomov MN, Shalek AK, Iannacone M, Ciaccio MF, et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell. 2011;147:853–67.

    Article  Google Scholar 

  40. Yosef N, Shalek AK, Gaublomme JT, Jin H, Lee Y, Awasthi A, et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature. 2013;496:461–8.

    Article  Google Scholar 

  41. Kowalczyk MS, Tirosh I, Heckl D, Rao TN, Dixit A, Haas BJ, et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 2015;25:1860–72.

    Article  Google Scholar 

  42. Shay T, Jojic V, Zuk O, Rothamel K, Puyraimond-Zemmour D, Feng T, et al. Conservation and divergence in the transcriptional programs of the human and mouse immune systems. Proc Natl Acad Sci U S A. 2013;110:2946–51.

    Article  Google Scholar 

  43. Brandes M, Klauschen F, Kuchen S, Germain RN. A systems analysis identifies a feedforward inflammatory circuit leading to lethal influenza infection. Cell. 2013;154:197–212.

    Article  Google Scholar 

  44. Nakaya HI, Pulendran B. Vaccinology in the era of high-throughput biology. Philos Trans R Soc Lond Ser B Biol Sci. 2015;370:20140146.

    Article  Google Scholar 

  45. Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10:116–25.

    Article  Google Scholar 

  46. Ravindran R, Khan N, Nakaya HI, Li S, Loebbermann J, Maddur MS, et al. Vaccine activation of the nutrient sensor GCN2 in dendritic cells enhances antigen presentation. Science. 2014;343:313–7.

    Article  Google Scholar 

  47. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, et al. Systems biology of vaccination for seasonal influenza in humans. Nat Immunol. 2011;12:786–95.

    Article  Google Scholar 

  48. Bucasas KL, Franco LM, Shaw CA, Bray MS, Wells JM, Niño D, et al. Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J Infect Dis. 2011;203:921–9.

    Article  Google Scholar 

  49. Zak DE, Andersen-Nissen E, Peterson ER, Sato A, Hamilton MK, Borgerding J, et al. Merck Ad5/HIV induces broad innate immune activation that predicts CD8+ T-cell responses but is attenuated by preexisting Ad5 immunity. Proc Natl Acad Sci U S A. 2012;109:E3503–12.

    Article  Google Scholar 

  50. Vahey MT, Wang Z, Kester KE, Cummings J, Heppner DG, Nau ME, et al. Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine. J Infect Dis. 2010;201:580–9.

    Article  Google Scholar 

  51. Reif DM, Motsinger-Reif AA, McKinney BA, Rock MT, Crowe JE, Moore JH. Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination. Genes Immun. 2009;10:112–9.

    Article  Google Scholar 

  52. Gunawardena J. Beware the tail that wags the dog: informal and formal models in biology. Mol Biol Cell. 2014;25:3441–4.

    Article  Google Scholar 

  53. Motta S, Pappalardo F. Mathematical modeling of biological systems. Brief Bioinf. 2013;14:411–22.

    Article  Google Scholar 

  54. Le Novère N. Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet. 2015;16:146–58.

    Article  Google Scholar 

  55. Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44:1–12.

    Article  Google Scholar 

  56. Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA. Logic-based models for the analysis of cell signaling networks. Biochemistry. 2010;49:3216–24.

    Article  Google Scholar 

  57. Hoffmann A, Levchenko A, Scott ML, Baltimore D. The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation. Science. 2002;298:1241–5.

    Article  Google Scholar 

  58. Altan-Bonnet G, Germain RN. Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 2005;3:e356.

    Article  Google Scholar 

  59. Bains I, Thiébaut R, Yates AJ, Callard R. Quantifying thymic export: combining models of naive T cell proliferation and TCR excision circle dynamics gives an explicit measure of thymic output. J Immunol. 2009;183:4329–36.

    Article  Google Scholar 

  60. Mayer H, Zaenker KS, An Der Heiden U. A basic mathematical model of the immune response. Chaos. 1995;5:155–61.

    Article  Google Scholar 

  61. Polak ME, Ung CY, Masapust J, Freeman TC, Ardern-Jones MR. Petri Net computational modelling of Langerhans cell interferon regulatory factor network predicts their role in T cell activation. Sci Rep. 2017;7:668.

    Article  Google Scholar 

  62. Mendoza L. A network model for the control of the differentiation process in Th cells. Biosystems. 2006;84:101–14.

    Article  Google Scholar 

  63. Zhang R, Shah MV, Yang J, Nyland SB, Liu X, Yun JK, et al. Network model of survival signaling in large granular lymphocyte leukemia. Proc Natl Acad Sci U S A. 2008;105:16308–13.

    Article  Google Scholar 

  64. Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris MK, et al. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol. 2012;6:133.

    Article  Google Scholar 

  65. Batt G, Besson B, Ciron P-E, de Jong H, Dumas E, Geiselmann J, et al. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. Methods Mol Biol. 2012;804:439–62.

    Article  Google Scholar 

  66. Müssel C, Hopfensitz M, Kestler HA. BoolNet – an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics. 2010;26:1378–80.

    Article  Google Scholar 

  67. Benoist C, Germain RN, Mathis D. A plaidoyer for “systems immunology”. Immunol Rev. 2006;210:229–34.

    Article  Google Scholar 

  68. Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol. 2007;3:78.

    Article  Google Scholar 

  69. Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5:101–13.

    Article  Google Scholar 

  70. Carter SL, Brechbühler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20:2242–50.

    Article  Google Scholar 

  71. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:17.

    Article  MathSciNet  Google Scholar 

  72. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50.

    Article  Google Scholar 

  73. Amar D, Safer H, Shamir R. Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput Biol. 2013;9:e1002955.

    Article  Google Scholar 

  74. Watson M. CoXpress: differential co-expression in gene expression data. BMC Bioinform. 2006;7:509.

    Article  Google Scholar 

  75. Ha MJ, Baladandayuthapani V, Do K-A. DINGO: differential network analysis in genomics. Bioinformatics. 2015;31:3413–20.

    Article  Google Scholar 

  76. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39:D691–7.

    Article  Google Scholar 

  77. Dolinski K, Troyanskaya OG. Implications of Big Data for cell biology. Mol Biol Cell. 2015;26:2575–8.

    Article  Google Scholar 

  78. Li B, Tsoi LC, Swindell WR, Gudjonsson JE, Tejasvi T, Johnston A, et al. Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol. 2014;134:1828–38.

    Article  Google Scholar 

  79. Gardinassi LG, Garcia GR, Costa CHN, Costa Silva V, de Miranda Santos IKF. Blood transcriptional profiling reveals immunological signatures of distinct states of infection of humans with Leishmania infantum. PLoS Negl Trop Dis. 2016;10:e0005123.

    Article  Google Scholar 

  80. Pandey G, Cohain A, Miller J, Merad M. Decoding dendritic cell function through module and network analysis. J Immunol Methods. 2013;387:71–80.

    Article  Google Scholar 

  81. Berry MPR, Graham CM, McNab FW, Xu Z, Bloch SAA, Oni T, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466:973–7.

    Article  Google Scholar 

  82. Mejias A, Dimo B, Suarez NM, Garcia C, Suarez-Arrabal MC, Jartti T, et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS Med. 2013;10:e1001549.

    Article  Google Scholar 

  83. Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 2013;8:2502–15.

    Article  Google Scholar 

  84. Jee J, Rozowsky J, Yip KY, Lochovsky L, Bjornson R, Zhong G, et al. ACT: aggregation and correlation toolbox for analyses of genome tracks. Bioinformatics. 2011;27:1152–4.

    Article  Google Scholar 

  85. Hunter PJ, Crampin EJ, Nielsen PMF. Bioinformatics, multiscale modeling and the IUPS Physiome project. Brief Bioinf. 2008;9:333–43.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil

    Melissa Lever, Thiago C. Hirata, Pedro S. T. Russo & Helder I. Nakaya

Authors
  1. Melissa Lever
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thiago C. Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro S. T. Russo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helder I. Nakaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helder I. Nakaya .

Editor information

Editors and Affiliations

  1. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva

  2. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Floriano Paes Silva Junior

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lever, M., Hirata, T.C., Russo, P.S.T., Nakaya, H.I. (2018). Systems Immunology. In: Alves Barbosa da Silva, F., Carels, N., Paes Silva Junior, F. (eds) Theoretical and Applied Aspects of Systems Biology. Computational Biology, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-74974-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74974-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74973-0

  • Online ISBN: 978-3-319-74974-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation